Team for Research in
Ubiquitous Secure Technology

Automated Tracking of Pallets in Warehouses: Beacon Layout and Asymmetric Ultrasound Observation Models,
Menasheh Fogel, Nathan Burkhart, Hongliang Ren, Jeremy Schiff, Max Meng, Ken Goldberg

Citation
Menasheh Fogel, Nathan Burkhart, Hongliang Ren, Jeremy Schiff, Max Meng, Ken Goldberg. "Automated Tracking of Pallets in Warehouses: Beacon Layout and Asymmetric Ultrasound Observation Models,". Third IEEE Conference on Automation Science and Engineering (CASE),, September, 2007.

Abstract
We consider the use of wireless sensor networks to automatically track “perceptive pallets” of materials in warehouses for the purpose of monitoring volumetric and spatial constraints. A combination of radio frequency and ultrasound chirping produces position estimates that are noisy and prone to error. To address this, we measure and characterize the ultrasound response from standard “Cricket” wireless sensor motes and beacons. We develop a non-parametric particle filtering approach to estimate trajectories of moving motes and introduce two asymmetric observation models that incorporate measured cardioid-shaped response patterns of ultrasound. We use simulation to study the effects of mote placement: position error as a function of ceiling height and beacon density, and then perform physical experiments to evaluate the effectiveness of asymmetric vs. symmetric observation models for pallet tracking. Experiments suggest that asymmetric observation models can improve position estimates by as much as 11%.

Electronic downloads

Citation formats  
  • HTML
    Menasheh Fogel, Nathan Burkhart, Hongliang Ren, Jeremy
    Schiff, Max Meng, Ken Goldberg. <a
    href="http://www.truststc.org/pubs/707.html"
    >Automated Tracking of Pallets in Warehouses: Beacon
    Layout and Asymmetric Ultrasound Observation
    Models,</a>, Third IEEE Conference on Automation
    Science and Engineering (CASE),, September, 2007.
  • Plain text
    Menasheh Fogel, Nathan Burkhart, Hongliang Ren, Jeremy
    Schiff, Max Meng, Ken Goldberg. "Automated Tracking of
    Pallets in Warehouses: Beacon Layout and Asymmetric
    Ultrasound Observation Models,". Third IEEE Conference
    on Automation Science and Engineering (CASE),, September,
    2007.
  • BibTeX
    @inproceedings{FogelBurkhartRenSchiffMengGoldberg07_AutomatedTrackingOfPalletsInWarehousesBeaconLayoutAsymmetric,
        author = {Menasheh Fogel and Nathan Burkhart and Hongliang
                  Ren and Jeremy Schiff and Max Meng and Ken Goldberg},
        title = {Automated Tracking of Pallets in Warehouses:
                  Beacon Layout and Asymmetric Ultrasound
                  Observation Models,},
        booktitle = {Third IEEE Conference on Automation Science and
                  Engineering (CASE),},
        month = {September},
        year = {2007},
        abstract = {We consider the use of wireless sensor networks to
                  automatically track âperceptive palletsâ of
                  materials in warehouses for the purpose of
                  monitoring volumetric and spatial constraints. A
                  combination of radio frequency and ultrasound
                  chirping produces position estimates that are
                  noisy and prone to error. To address this, we
                  measure and characterize the ultrasound response
                  from standard âCricketâ wireless sensor motes
                  and beacons. We develop a non-parametric particle
                  filtering approach to estimate trajectories of
                  moving motes and introduce two asymmetric
                  observation models that incorporate measured
                  cardioid-shaped response patterns of ultrasound.
                  We use simulation to study the effects of mote
                  placement: position error as a function of ceiling
                  height and beacon density, and then perform
                  physical experiments to evaluate the effectiveness
                  of asymmetric vs. symmetric observation models for
                  pallet tracking. Experiments suggest that
                  asymmetric observation models can improve position
                  estimates by as much as 11%.},
        URL = {http://www.truststc.org/pubs/707.html}
    }
    

Posted by Jessica Gamble on 5 Apr 2010.
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